{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3eaa1562-b210-4b24-b8d1-cc21ac491c80",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def getsin(A,f,t):\n",
    "    return A*np.sin(-2*math.pi*f*t)\n",
    "def getcos(A,f,t):\n",
    "    return A*np.cos(-2*math.pi*f*t)\n",
    "def denoise(arr,thresh):\n",
    "    arr[arr<=thresh]=0\n",
    "    return arr\n",
    "num=3000\n",
    "t=np.linspace(0,1,num)\n",
    "s1=getsin(1.5,30 ,t)\n",
    "s2=getcos(3  ,5  ,t)\n",
    "s3=getsin(10 ,100,t)\n",
    "s4=getcos(20 ,120,t)\n",
    "#混合波形\n",
    "m=s1+s2+s3+s4\n",
    "\n",
    "#循环算法\n",
    "fCoefs1=np.zeros(num,dtype='complex')\n",
    "for f in range(num):\n",
    "    p=m*np.exp(-1j*2*math.pi*f*t)\n",
    "    fCoefs1[f]=np.sum(p)\n",
    "A1_list=2*np.abs(fCoefs1/num)\n",
    "f1=np.fft.fftfreq(len(A1_list),1/num)\n",
    "A1_shift=np.fft.fftshift(A1_list)\n",
    "f1_shift=np.fft.fftshift(f1)\n",
    "\n",
    "#fft.fft方法\n",
    "fCoefs2=np.fft.fft(m,num)\n",
    "A2_list=2*np.abs(fCoefs2/num)\n",
    "f2=np.fft.fftfreq(len(A2_list),1/num)\n",
    "A2_shift=np.fft.fftshift(A2_list)\n",
    "f2_shift=np.fft.fftshift(f2)\n",
    "\n",
    "fg,ax=plt.subplots(2,2,figsize=(10,8))\n",
    "ax[0,0].grid()\n",
    "ax[0,0].stem(f1_shift,A1_shift)\n",
    "ax[0,0].set_xlim([-150,150])\n",
    "ax[0,0].set_ylim([-0.5, 21])\n",
    "ax[0,0].set_title(\"Amplitude plot of cyclic method\")\n",
    "\n",
    "A1_shift[(f1_shift > 110) | (f1_shift < -110)] = 0\n",
    "ax[0,1].grid()\n",
    "ax[0,1].stem(f1_shift,A1_shift)\n",
    "ax[0,1].set_xlim([-150,150])\n",
    "ax[0,1].set_ylim([-0.5, 21])\n",
    "\n",
    "\n",
    "ax[1,0].grid()\n",
    "ax[1,0].stem(f2_shift,A2_shift)\n",
    "ax[1,0].set_xlim([-150,150])\n",
    "ax[1,0].set_ylim([-0.5, 21])\n",
    "ax[1,0].set_title(\"Amplitude plot of fft method\")\n",
    "\n",
    "denoise(A2_shift,1)\n",
    "A2_shift[(f2_shift > 110) | (f2_shift < -110)] = 0\n",
    "ax[1,1].grid()\n",
    "ax[1,1].stem(f2_shift,A2_shift)\n",
    "ax[1,1].set_xlim([-150,150])\n",
    "ax[1,1].set_ylim([-0.5, 21])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab341580-6639-44e7-a790-6dfa0c3e80e5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
